Career
Senior Machine Learning Engineer
Operations
|
Permanent
Operations
Permanent
About Us
Do you want to be part of Thailand banking transformation? Data is the core of the new financial services era, and we are open for the opportunity to be part to drive this change at the core.
SCB DATAx is a new venture of the Siam Commercial Bank (SCB) holdings, a leading financial services and digital services holdings in Thailand and ASEAN.
As part of the transformation of SCB into a group of product and technology companies, under the SCBx brand, SCB DATAx is the technology company to centralize data and provides AI and data science services and products to the group.
With a leading-edge cloud native data & AI platform, our vision is to support the group to providing everyone in our region with the opportunity to prosper.
We work on forward-thinking challenges of centralizing, analyzing and sharing information. We collaborate with companies and experts in many different domains, embrace diversity and all that while having a good laugh and joy in work.
Discover job openings on our career page. To apply, email with the role's title as the subject, attach your CV, and specify your contact information. We're eager to learn more about you.
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Benefits
Other
Preferred Qualifications
Experience with real-time Generative AI applications
Open-source contributions
Technical leadership experience
Qualifications
5+ years of hands-on ML engineering in production environments
Bachelor’s degree in computer science, engineering, or related field
Production ML system architecture and implementation
Feature engineering and feature store design
ML code performance tuning
Model optimization and deployment strategies
Databricks Suite: (Delta Lake, MLflow, Feature Store, Unity Catalog, Workflow orchestration)
Cloud platform expertise (preferably Azure)
Kubernetes orchestration
Infrastructure as Code (Terraform)
CI/CD pipeline design (GitHub Actions)
Advanced Python development
Test-driven development practices
SQL and data modeling
Responsibilities
Architect and implement production ML systems at scale
Lead MLOps infrastructure decisions and establish engineering best practices
Design robust ML monitoring and observability solutions
Build and optimize feature stores and model serving platforms
Mentor team members on ML engineering practices
Design and implement both batch and real-time ML systems
Lead cross-functional ML initiatives
Establish ML engineering best practices
Drive adoption of MLOps tools and practices
About Team & Role
We're seeking an experienced ML to architect, lead and implement production-grade machine learning pipelines & system. You'll drive best practices for deploying and maintaining models efficiently, enabling our teams to leverage advanced solutions at scale.